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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Indonesian Journal of Electrical Engineering and Computer Science
Imran Mahmud
Daffodil International University
Computer Science & IT Electrical & Electronics Engineering

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Modelling turn away intention of information technology professionals in Bangladesh: a partial least squares approach Md. Shohel Arman; Rozina Akter; Imran Mahmud; T. Ramayah
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.67 KB) | DOI: 10.11591/ijece.v10i5.pp4973-4981

Abstract

Despite, Bangladesh produces many IT graduates each year but only one tenth of total graduates contribute in IT development sector. In order to keep the contribution to economy through IT development, it is crucial for IT industry to know the factors that influence turn away of IT graduates. In this paper, building upon role stress theory, we develop a research model to explore the influence of workplace exhaustion and threat of professional obsolescence (TPO). Data were gathered from 185 IT professionals from 15 different IT companies through survey questionnaire. The structural equation modelling technique was used to test the paths. The results suggests that strong influence of TPO on turn-away intentions. Result also suggests significant roles of work overload, family-career conflict and control over career and workplace exhaustion on turn away intention. This paper contributes to the body of work dedicated to helping us better understand the turn away behaviour from the workplace exhaustion and TPO perspectives. From the viewpoint of practice, this research sheds light on some of the challenges that the IT industry might face when making strategy and policy to control turn away from IT profession in Bangladesh
Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach Abu Kowshir Bitto; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3834

Abstract

Among the most important systems in the body is the eyes. Although their small stature, humans are unable to imagine existence without it. The human optic is safe against dust particles by a narrow layer called the conjunctiva. It prevents friction during the opening and shutting of the eye by acting as a lubricant. A cataract is an opacification of the eye's lens. There are various forms of eye problems. Because the visual system is the most important of the four sensory organs, external eye abnormalities must be detected early. The classification technique can be used in a variety of situations. A few of these uses are in the healthcare profession. We use visual geometry group (VGG-16), ResNet-50, and Inception-v3 architectures of convolutional neural networks (CNNs) to distinguish between normal eyes, conjunctivitis eyes, and cataract eyes throughout this paper. With a detection time of 485 seconds, Inception-v3 is the most accurate at detecting eye disease, with a 97.08% accuracy, ResNet-50 performs the second-highest accuracy with 95.68% with 1090 seconds and lastly, VGG-16 performs 95.48% accuracy taking the highest time of 2510 seconds to detect eye diseases.
Modelling consumer’s intention to use IoT devices: role of technophilia Nusrat Jahan; Md. Abu Hosen Shawon; Farzana Sadia; Dilara Khanom Nitu; Md. Enam Kobir Ribon; Imran Mahmud
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp612-620

Abstract

The present study has been conducted to examine whether skills and general technology-related value (GTV) required to operate the internet of things (IoT). This study also investigates is there any effect of technophilia to adopt IoT. The research method we use in this quantitative study was the sample survey. For investigating results, 352 surveys were conducted where 26 surveys were led through online and 292 surveys were distributed to different age groups. The proposed model was examined using partial least square structural equation model where the results revealed that IoT skills and General knowledge on technology directly contribute to technophilia which covers behavioural, emotional, and cognitive aspects. That is if people have a fascination for new technologies then they are willing to use IoT.
Analysing most efficient deep learning model to detect COVID-19 from computer tomography images F.M. Javed Mehedi Shamrat; Sovon Chakraborty; Rasel Ahammad; Tanzil Mahbub Shitab; Md.Aslam Kazi; Alamin Hossain; Imran Mahmud
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp462-471

Abstract

COVID-19 illness has a detrimental impact on the respiratory system, and the severity of the infection may be determined utilizing a selected imaging technique. Chest computer tomography (CT) imaging is a reliable diagnostic technique for finding COVID-19 early and slowing its progression. Recent research shows that deep learning algorithms, particularly convolutional neural network (CNN), may accurately diagnose COVID-19 using lung CT scan images. But in an emergency, detection accuracy simply is not enough. Determinants of data loss and classification completion time play a critical element. This study addresses the issue by finding the most efficient CNN model with the least data loss and classification time. Eight deep learning models, including Max Pooling 2D, Average Pooling 2D, VGG19, VGG16, MobileNetV2, InceptionV3, AlexNet, NFNet using a dataset of 16000 CT scans image data of COVID-19 and non-COVID-19 are compared in the study. Using the confusion matrix, the performance of the models is compared and together with the data loss and completion time. It is observed from the research that MobileNetV2 provides the highest accurate result of 99.12% with the least data loss of 0.0504% in the lowest classification completion time of 16.5secs per epoch. Thus, employing MobileNetV2 gives the best and the quickest result in an emergency.
CryptoAR: scrutinizing the trend and market of cryptocurrency using machine learning approach on time series data Abu Kowshir Bitto; Imran Mahmud; Md. Hasan Imam Bijoy; Fatema Tuj Jannat; Md. Shohel Arman; Md. Mahfuj Hasan Shohug; Hasnur Jahan
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1684-1696

Abstract

Cryptocurrencies are encrypted digital or virtual money used to avoid counterfeiting and double spending. The scope of this study is to evaluate cryptocurrencies and forecast their price in the context of the currency rate trends. A public survey was conducted to determine which cryptocurrency is the most well-known among Bangladeshi people. According to the survey respondents, Bitcoin is the most famous cryptocurrency among the eight digital currencies. After that, we'll explore the four most well-known cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Tether token. The 'YFinance' python package collects our cryptocurrency dataset, and the relative strength index (RSI) is employed to investigate these cryptocurrencies. Autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) models are applied to our time-series data from 2015-1-1 to 2021-6-1. Using the 'closing' price and a simple moving average (SMA) graph, bitcoin and tether are identified as oversold or overbought cryptocurrencies. We employ the seasonal decomposed technique into the dataset before implementing the model, and the augmented dickey-fuller test (ADF) indicates too much seasonality in the dataset. The autoregressive (AR) model is the most accurate in predicting the price of Bitcoin, Ethereum, Litecoin, and Tether-token, with 97.21%, 96.04%, 95.8%, and 99.91% accuracy, consecutively.
Social crisis detection using Twitter based text mining-a machine learning approach Shoaib Rahman; Nusrat Jahan; Farzana Sadia; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.3957

Abstract

Social-media and blogs are increasingly used for social-communication, an idea and thought publishing platform. Public intentions, wisdom, problems, solutions, mental states are shared in social media. Text is being the best and the most common way to communicate over social networks. All kinds of data shared in social sites like Facebook, Twitter, and Microblogs. People from different pursuance uses these media to publish thoughts and convey messages through text. Consequently, occurrences in social life are rapidly discussed in social blogs in daily manner. This work aims at discovering ongoing social crisis from the Twitter data. Text mining technique and sentiment analysis were applied to detect the current social crisis from the social sites. Twitter data were collected to identify the recent social crisis. Furthermore, the identified crisis was compared to reputed newspapers. A hybrid method used to detect recent social issues resulted nicely. However, our proposed analysis shows identifying rate 89%, 95%, 83%, 53%, and 98% for the top 5 identified crisis accordingly in the date between 27 February and 11 March 2020. The strategy used in this study for the detection of recent social crisis will contribute to the social life and findings of crisis will be eliminated easily.
A predictive analysis framework of heart disease using machine learning approaches Shourav Molla; F. M. Javed Mehedi Shamrat; Raisul Islam Rafi; Umme Umaima; Md. Ariful Islam Arif; Shahed Hossain; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3942

Abstract

Heart diseaseis among the leading causes for death globally. Thus, early identification and treatment are indispensable to prevent the disease. In this work, we propose a framework based on machine learning algorithms to tackle such problems through the identification of risk variables associated to this disease. To ensure the success of our proposed model, influential data pre-processing and data transformation strategies are used to generate accurate data for the training model that utilizes the five most popular datasets (Hungarian, Stat log, Switzerland, Long Beach VA, and Cleveland) from UCI. The univariate feature selection technique is applied to identify essential features and during the training phase, classifiers, namely extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), gradient boosting (GB), and decision tree (DT), are deployed. Subsequently, various performance evaluations are measured to demonstrate accurate predictions using the introduced algorithms. The inclusion of Univariate results indicated that the DT classifier achieves a comparatively higher accuracy of around 97.75% than others. Thus, a machine learning approach is recognize, that can predict heart disease with high accuracy. Furthermore, the 10 attributes chosen are used to analyze the model's outcomes explainability, indicating which attributes are more significant in the model's outcome.
Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market Ashikur Rahaman; Abu Kowshir Bitto; Khalid Been Md. Badruzzaman Biplob; Md. Hasan Imam Bijoy; Nusrat Jahan; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4486

Abstract

As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.
Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning Abu Kowshir Bitto; Md. Hasan Imam Bijoy; Md. Shohel Arman; Imran Mahmud; Aka Das; Joy Majumder
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4135

Abstract

Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy of 89.64%. LSTM has the highest accuracy rate of the three DL algorithms, with an accuracy of 91.07%. Among ML and DL models, LSTM DL takes the lead to predict the sentiment.
Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images Abu Kowshir Bitto; Md. Hasan Imam Bijoy; Sabina Yesmin; Imran Mahmud; Md. Jueal Mia; Khalid Been Badruzzaman Biplob
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.872

Abstract

Abnormal brain tissue or cell growth is known as a brain tumor. One of the body's most intricate organs is the brain, where billions of cells work together. As a head tumor grows, the brain suffers damage due to its increasingly dense core. Magnetic resonance imaging, or MRI, is a type of medical imaging that enables radiologists to view the inside of body structures without the need for surgery. The image-based medical diagnosis expert system is crucial for a brain tumor patient. In this study, we combined two Magnetic Resonance Imaging (MRI)-based image datasets from Figshare and Kaggle to identify brain tumor MRI using a variety of convolutional neural network designs. To achieve competitive performance, we employ several data preprocessing techniques, such as resizing and enhancing contrast. The image augmentation techniques (E.g., rotated, width shifted, height shifted, shear shifted, and horizontally flipped) are used to increase data size, and five pre-trained models employed, including VGG-16, VGG-19, ResNet-50, Xception, and Inception-V3. The model with the highest accuracy, ResNet-50, performs at 96.76 percent. The model with the highest precision overall is Inception V3, with a precision score of 98.83 percent. ResNet-50 performs at 96.96% for F1-Score. The prominent accuracy of the implemented model, i.e., ResNet-50, compared with several earlier studies to validate the consequence of this introspection. The outcome of this study can be used in the medical diagnosis of brain tumors with an MRI-based expert system.
Co-Authors Abu Kowshir Bitto Aka Das Alamin Hossain Ashikur Rahaman Dilara Khanom Nitu F. M. Javed Mehedi Shamrat F.M. Javed Mehedi Shamrat Farzana Sadia Fatema Tuj Jannat Hasnur Jahan Joy Majumder Khalid Been Badruzzaman Biplob Khalid Been Md. Badruzzaman Biplob Md. Abu Hosen Shawon Md. Ariful Islam Arif Md. Enam Kobir Ribon Md. Hasan Imam Bijoy Md. Jueal Mia Md. Mahfuj Hasan Shohug Md. Shohel Arman Md.Aslam Kazi Nusrat Jahan Raisul Islam Rafi Rasel Ahammad Rozina Akter Sabina Yesmin Shahed Hossain Shoaib Rahman Shourav Molla Sovon Chakraborty T. Ramayah Tanzil Mahbub Shitab Umme Umaima